




"""simple docstring"""







import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  SCREAMING_SNAKE_CASE_	     =       None


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_	     =       {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
SCREAMING_SNAKE_CASE_	     =       {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       VOCAB_FILES_NAMES
   A_				      =       PRETRAINED_VOCAB_FILES_MAP
   A_				      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_				      =       ['''input_ids''', '''attention_mask''']
   A_				      =       TaTokenizer

   A_				      =       []







   def __init__(		self       ,					lowerCamelCase_=None       ,					lowerCamelCase_=None       ,					lowerCamelCase_="</s>"       ,					lowerCamelCase_="<unk>"       ,					lowerCamelCase_="<pad>"       ,					lowerCamelCase_=1_0_0       ,					lowerCamelCase_=None       ,					**lowerCamelCase_       ,					)				->   List[Any]:
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
           UpperCamelCase              =	[F'<extra_id_{i}>' for i in range(lowerCamelCase_)]
       elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra special tokens
           UpperCamelCase              =	len(set(filter(lambda lowerCamelCase_: bool('''extra_id_''' in str(lowerCamelCase_))       ,					lowerCamelCase_)))
           if extra_tokens != extra_ids:
               raise ValueError(
                   F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                   ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                   ''' tokens''')

       super().__init__(
           lowerCamelCase_       ,					tokenizer_file=lowerCamelCase_       ,					eos_token=lowerCamelCase_       ,					unk_token=lowerCamelCase_       ,					pad_token=lowerCamelCase_       ,					extra_ids=lowerCamelCase_       ,					additional_special_tokens=lowerCamelCase_       ,					**lowerCamelCase_       ,					)

       UpperCamelCase              =	vocab_file
       UpperCamelCase              =	False if not self.vocab_file else True
       UpperCamelCase              =	extra_ids







   @staticmethod
   def   UpperCAmelCase__			(		lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   List[str]:
       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
           UpperCamelCase              =	TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
               return init_max_model_length
           elif init_max_model_length is None:
               warnings.warn(
                   '''This tokenizer was incorrectly instantiated with a model max length of'''
                   F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                   ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                   ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                   F' {pretrained_model_name_or_path} automatically truncating your input to'
                   F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                   F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                   ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                   ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''       ,					lowerCamelCase_       ,					)

       return max_model_length







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   Tuple[str]:
       if not self.can_save_slow_tokenizer:
           raise ValueError(
               '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
               '''tokenizer.''')

       if not os.path.isdir(lowerCamelCase_):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory')
           return
       UpperCamelCase              =	os.path.join(
           lowerCamelCase_       ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

       if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCamelCase_):
           copyfile(self.vocab_file       ,					lowerCamelCase_)
           logger.info(F'Copy vocab file to {out_vocab_file}')

       return (out_vocab_file,)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   List[int]:
       UpperCamelCase              =	token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
           return self.prefix_tokens + token_ids_a
       else:
           UpperCamelCase              =	token_ids_a + [self.eos_token_id]
           return self.prefix_tokens + token_ids_a + token_ids_a







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   List[int]:
       UpperCamelCase              =	[self.eos_token_id]

       if token_ids_a is None:
           return len(token_ids_a + eos) * [0]
       return len(token_ids_a + eos + token_ids_a + eos) * [0]







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       return list(
           set(filter(lambda lowerCamelCase_: bool(re.search(R'''<extra_id_\d+>'''       ,					lowerCamelCase_)) is not None       ,					self.additional_special_tokens)))







   def   UpperCAmelCase__			(		self)				->   List[str]:
       return [self.convert_tokens_to_ids(lowerCamelCase_) for token in self.get_sentinel_tokens()]

import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


lowercase    :   List[str]						=			logging.get_logger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Dict	,  *a						:Tuple	,  **a						:List[Any]		)						->					None:
								warnings.warn(
								    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
								    " Please use PerceiverImageProcessor instead."	,  a	,  )
								super().__init__(*a	,  **a		)